arXiv Machine Learning
ChronoSpike: An Adaptive Spiking Graph Neural Network for Dynamic Graphs
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arXiv Machine Learning
Jun 15
SpikF-GO: Spiking Fourier Graph Operators for Multivariate Time Series Forecasting
arXiv:2606. 13901v1 Announce Type: new Abstract: Spiking Neural Networks (SNNs) have emerged as an energy-efficient alternative to conventional neural networks, demonstrating strong performance in computer vision and robotics.
arXiv AI
2d ago
NeuronSpark: Selective Spiking State Space Models with Adaptive Computation for Language Modeling
NeuronSpark is a self‑attention‑free language model that uses input‑dependent spiking membrane dynamics, allowing each neuron’s decay rate, gain, and firing threshold to adapt to the current token. This selective spiking approach enables billion‑parameter pretraining from random initialization without relying on backbone transfer, conversion, or distillation, achieving competitive zero‑shot performance and a fast–slow hierarchy. On a 1.1B‑parameter, 2.1B‑token training run, NeuronSpark attains a 40.0 eight‑task zero‑shot average, surpasses comparable‑budget Pythia/Mamba checkpoints, and demonstrates significant energy and cycle reductions on FPGA implementations.
Hugging Face Trending Papers
Jul 29
The Sparsity Ceiling: Where Spiking Networks Can and Cannot Trade Activity for Energy
Spiking neural networks (SNNs) are promoted as an energy-efficient substrate because sparse, event-driven activity replaces dense multiply-accumulates with cheap accumulates. We argue the energy dividend of sparsity is not a property of SNNs but of the task.
arXiv Machine Learning
Jun 26
Learning Long-Range Dependencies with Temporal Predictive Coding
arXiv:2602. 18131v2 Announce Type: replace Abstract: Temporal Predictive Coding provides a layer-local, parallelisable mechanism for learning in recurrent systems, making it an attractive candidate for online local learning on neuromorphic and edge hardware.
arXiv Machine Learning
Jul 30
The Sparsity Ceiling: Where Spiking Networks Can and Cannot Trade Activity for Energy
arXiv:2607. 26648v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) are promoted as an energy-efficient substrate because sparse, event-driven activity replaces dense multiply-accumulates with cheap accumulates.
arXiv Computation and Language
Sep 15
URCHIN: A Horizontal Spiking Language Model for Data-Constrained Pretraining
arXiv:2609.13899v1 Announce Type: cross Abstract: The BabyLM challenge measures how much language a model can learn from developmentally-plausible, child-scale data rather than internet-scale corpora...